BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Findings of the 2009 workshop on statistical machine translation
StatMT '09 Proceedings of the Fourth Workshop on Statistical Machine Translation
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
TESLA: translation evaluation of sentences with linear-programming-based analysis
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
Automatic evaluation of Chinese translation output: word-level or character-level?
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Findings of the 2011 Workshop on Statistical Machine Translation
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
TESLA at WMT 2011: translation evaluation and tunable metric
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
Better evaluation metrics lead to better machine translation
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Substring-based machine translation
Machine Translation
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In this work, we introduce the TESLA-CELAB metric (Translation Evaluation of Sentences with Linear-programming-based Analysis -- Character-level Evaluation for Languages with Ambiguous word Boundaries) for automatic machine translation evaluation. For languages such as Chinese where words usually have meaningful internal structure and word boundaries are often fuzzy, TESLA-CELAB acknowledges the advantage of character-level evaluation over word-level evaluation. By reformulating the problem in the linear programming framework, TESLA-CELAB addresses several drawbacks of the character-level metrics, in particular the modeling of synonyms spanning multiple characters. We show empirically that TESLA-CELAB significantly outperforms character-level BLEU in the English-Chinese translation evaluation tasks.